While, for many years, computer scientists assumed that many complex tasks carried about by humans, including recognition and characterization of objects in images, would be rapidly automated by various techniques and approaches that were referred to as “artificial intelligence” (“AI”), the optimistic forecasts for optimization were not, in most cases, reflected in actual technical and scientific developments and progress. Many seemingly tractable computational problems proved to be far more complex than originally imagined and the hardware platforms, despite rapid evolution in capabilities and capacities, fell short of the computational bandwidths needed for automation of the complex tasks.
During the past 10 years, significant advances in distributed computing, including the emergence of cloud computing, have placed enormous computational bandwidth at the disposal of computational-bandwidth consumers, and is now routinely used for data analytics, scientific computation, web-site hosting, and for carrying out AI computations. However, even with the computational-bandwidth constraints relieved by massive distributed-computing systems, many problems remain difficult. Currently, designers and developers of systems that automate tasks formerly assumed to require human intelligence, including face recognition and identification of objects in images, continue to seek methods and subsystems that effectively harness the available computational bandwidth to address complex problem domains.